init

transformers/tests/models/gemma3n/test_feature_extraction_gemma3n.py

@@ -0,0 +1,299 @@
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import itertools
+import os
+import random
+import tempfile
+import unittest
+from collections.abc import Sequence
+from typing import Optional
+
+import numpy as np
+from parameterized import parameterized
+
+from transformers.models.gemma3n import Gemma3nAudioFeatureExtractor
+from transformers.testing_utils import (
+    check_json_file_has_correct_format,
+    require_torch,
+)
+from transformers.utils.import_utils import is_torch_available
+
+from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
+
+
+if is_torch_available():
+    pass
+
+global_rng = random.Random()
+
+MAX_LENGTH_FOR_TESTING = 512
+
+
+def floats_list(shape, scale=1.0, rng=None):
+    """Creates a random float32 tensor"""
+    if rng is None:
+        rng = global_rng
+
+    values = []
+    for _ in range(shape[0]):
+        values.append([])
+        for _ in range(shape[1]):
+            values[-1].append(rng.random() * scale)
+
+    return values
+
+
+class Gemma3nAudioFeatureExtractionTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        min_seq_length=400,
+        max_seq_length=2000,
+        feature_size: int = 128,
+        sampling_rate: int = 16_000,
+        padding_value: float = 0.0,
+        return_attention_mask: bool = False,
+        # ignore hop_length / frame_length for now, as ms -> length conversion causes issues with serialization tests
+        # frame_length_ms: float = 32.0,
+        # hop_length: float = 10.0,
+        min_frequency: float = 125.0,
+        max_frequency: float = 7600.0,
+        preemphasis: float = 0.97,
+        preemphasis_htk_flavor: bool = True,
+        fft_overdrive: bool = True,
+        dither: float = 0.0,
+        input_scale_factor: float = 1.0,
+        mel_floor: float = 1e-5,
+        per_bin_mean: Optional[Sequence[float]] = None,
+        per_bin_stddev: Optional[Sequence[float]] = None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_seq_length = min_seq_length
+        self.max_seq_length = max_seq_length
+        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
+        self.feature_size = feature_size
+        self.sampling_rate = sampling_rate
+        self.padding_value = padding_value
+        self.return_attention_mask = return_attention_mask
+        # ignore hop_length / frame_length for now, as ms -> length conversion causes issues with serialization tests
+        # self.frame_length_ms = frame_length_ms
+        # self.hop_length = hop_length
+        self.min_frequency = min_frequency
+        self.max_frequency = max_frequency
+        self.preemphasis = preemphasis
+        self.preemphasis_htk_flavor = preemphasis_htk_flavor
+        self.fft_overdrive = fft_overdrive
+        self.dither = dither
+        self.input_scale_factor = input_scale_factor
+        self.mel_floor = mel_floor
+        self.per_bin_mean = per_bin_mean
+        self.per_bin_stddev = per_bin_stddev
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "feature_size": self.feature_size,
+            "sampling_rate": self.sampling_rate,
+            "padding_value": self.padding_value,
+            "return_attention_mask": self.return_attention_mask,
+            "min_frequency": self.min_frequency,
+            "max_frequency": self.max_frequency,
+            "preemphasis": self.preemphasis,
+            "preemphasis_htk_flavor": self.preemphasis_htk_flavor,
+            "fft_overdrive": self.fft_overdrive,
+            "dither": self.dither,
+            "input_scale_factor": self.input_scale_factor,
+            "mel_floor": self.mel_floor,
+            "per_bin_mean": self.per_bin_mean,
+            "per_bin_stddev": self.per_bin_stddev,
+        }
+
+    def prepare_inputs_for_common(self, equal_length=False, numpify=False):
+        def _flatten(list_of_lists):
+            return list(itertools.chain(*list_of_lists))
+
+        if equal_length:
+            speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
+        else:
+            # make sure that inputs increase in size
+            speech_inputs = [
+                floats_list((x, self.feature_size))
+                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
+            ]
+        if numpify:
+            speech_inputs = [np.asarray(x) for x in speech_inputs]
+        return speech_inputs
+
+
+class Gemma3nAudioFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
+    feature_extraction_class = Gemma3nAudioFeatureExtractor
+
+    def setUp(self):
+        self.feat_extract_tester = Gemma3nAudioFeatureExtractionTester(self)
+
+    def test_feat_extract_from_and_save_pretrained(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        mel_1 = feat_extract_first.mel_filters
+        mel_2 = feat_extract_second.mel_filters
+        self.assertTrue(np.allclose(mel_1, mel_2))
+        self.assertEqual(dict_first, dict_second)
+
+    def test_feat_extract_to_json_file(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            json_file_path = os.path.join(tmpdirname, "feat_extract.json")
+            feat_extract_first.to_json_file(json_file_path)
+            feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        mel_1 = feat_extract_first.mel_filters
+        mel_2 = feat_extract_second.mel_filters
+        self.assertTrue(np.allclose(mel_1, mel_2))
+        self.assertEqual(dict_first, dict_second)
+
+    def test_feat_extract_from_pretrained_kwargs(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(
+                tmpdirname, feature_size=2 * self.feat_extract_dict["feature_size"]
+            )
+
+        mel_1 = feat_extract_first.mel_filters
+        mel_2 = feat_extract_second.mel_filters
+        self.assertTrue(2 * mel_1.shape[1] == mel_2.shape[1])
+
+    @parameterized.expand(
+        [
+            ([floats_list((1, x))[0] for x in range(800, 1400, 200)],),
+            ([floats_list((1, x))[0] for x in (800, 800, 800)],),
+            ([floats_list((1, x))[0] for x in range(200, (MAX_LENGTH_FOR_TESTING + 500), 200)], True),
+        ]
+    )
+    def test_call(self, audio_inputs, test_truncation=False):
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        np_audio_inputs = [np.asarray(audio_input) for audio_input in audio_inputs]
+
+        input_features = feature_extractor(np_audio_inputs, padding="max_length", return_tensors="np").input_features
+        self.assertTrue(input_features.ndim == 3)
+        # input_features.shape should be (batch, num_frames, n_mels) ~= (batch, num_frames, feature_size)
+        # 480_000 is the max_length that inputs are padded to. we use that to calculate num_frames
+        expected_num_frames = (480_000 - feature_extractor.frame_length) // (feature_extractor.hop_length) + 1
+        self.assertTrue(
+            input_features.shape[-2] == expected_num_frames,
+            f"no match: {input_features.shape[-1]} vs {expected_num_frames}",
+        )
+        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
+
+        encoded_sequences_1 = feature_extractor(audio_inputs, return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_audio_inputs, return_tensors="np").input_features
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
+
+        if test_truncation:
+            audio_inputs_truncated = [x[:MAX_LENGTH_FOR_TESTING] for x in audio_inputs]
+            np_audio_inputs_truncated = [np.asarray(audio_input) for audio_input in audio_inputs_truncated]
+
+            encoded_sequences_1 = feature_extractor(
+                audio_inputs_truncated, max_length=MAX_LENGTH_FOR_TESTING, return_tensors="np"
+            ).input_features
+            encoded_sequences_2 = feature_extractor(
+                np_audio_inputs_truncated, max_length=MAX_LENGTH_FOR_TESTING, return_tensors="np"
+            ).input_features
+            for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
+                self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
+
+    def test_audio_features_attn_mask_consistent(self):
+        # regression test for https://github.com/huggingface/transformers/issues/39911
+        # Test input_features and input_features_mask have consistent shape
+        np.random.seed(42)
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        for i in [512, 640, 1024]:
+            audio = np.random.randn(i)
+            mm_data = {
+                "raw_speech": [audio],
+                "sampling_rate": 16000,
+            }
+            inputs = feature_extractor(**mm_data, return_tensors="np")
+            out = inputs["input_features"]
+            mask = inputs["input_features_mask"]
+
+            assert out.ndim == 3
+            assert mask.ndim == 2
+            assert out.shape[:2] == mask.shape[:2]
+
+    def test_dither(self):
+        np.random.seed(42)  # seed the dithering randn()
+
+        # Tests that features with and without little dithering are similar, but not the same
+        dict_no_dither = self.feat_extract_tester.prepare_feat_extract_dict()
+        dict_no_dither["dither"] = 0.0
+
+        dict_dither = self.feat_extract_tester.prepare_feat_extract_dict()
+        dict_dither["dither"] = 0.00003  # approx. 1/32k
+
+        feature_extractor_no_dither = self.feature_extraction_class(**dict_no_dither)
+        feature_extractor_dither = self.feature_extraction_class(**dict_dither)
+
+        # create three inputs of length 800, 1000, and 1200
+        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
+
+        # compute features
+        input_features_no_dither = feature_extractor_no_dither(
+            np_speech_inputs, padding=True, return_tensors="np", sampling_rate=dict_no_dither["sampling_rate"]
+        ).input_features
+        input_features_dither = feature_extractor_dither(
+            np_speech_inputs, padding=True, return_tensors="np", sampling_rate=dict_dither["sampling_rate"]
+        ).input_features
+
+        # test there is a difference between features (there's added noise to input signal)
+        diff = input_features_dither - input_features_no_dither
+
+        # features are not identical
+        assert np.abs(diff).mean() > 1e-6
+        # features are not too different
+        # the heuristic value `7e-4` is obtained by running 50000 times (maximal value is around 3e-4).
+        assert np.abs(diff).mean() < 7e-4
+        # the heuristic value `8e-1` is obtained by running 50000 times (maximal value is around 5e-1).
+        assert np.abs(diff).max() < 8e-1
+
+    @require_torch
+    def test_double_precision_pad(self):
+        import torch
+
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
+        py_speech_inputs = np_speech_inputs.tolist()
+
+        for inputs in [py_speech_inputs, np_speech_inputs]:
+            np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
+            self.assertTrue(np_processed.input_features.dtype == np.float32)
+            pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
+            self.assertTrue(pt_processed.input_features.dtype == torch.float32)

transformers/tests/models/gemma3n/test_processing_gemma3n.py

@@ -0,0 +1,166 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+from parameterized import parameterized
+
+from transformers import GemmaTokenizerFast, SiglipImageProcessorFast, is_speech_available
+from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio, require_vision
+
+from .test_feature_extraction_gemma3n import floats_list
+
+
+if is_speech_available():
+    from transformers.models.gemma3n import Gemma3nAudioFeatureExtractor, Gemma3nProcessor
+
+
+# TODO: omni-modal processor can't run tests from `ProcessorTesterMixin`
+@require_torch
+@require_torchaudio
+@require_vision
+@require_sentencepiece
+class Gemma3nProcessorTest(unittest.TestCase):
+    def setUp(self):
+        # TODO: update to google?
+        self.model_id = "hf-internal-testing/namespace-google-repo_name-gemma-3n-E4B-it"
+        self.tmpdirname = tempfile.mkdtemp(suffix="gemma3n")
+        self.maxDiff = None
+
+    def get_tokenizer(self, **kwargs):
+        return GemmaTokenizerFast.from_pretrained(self.model_id, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return Gemma3nAudioFeatureExtractor.from_pretrained(self.model_id, **kwargs)
+
+    def get_image_processor(self, **kwargs):
+        return SiglipImageProcessorFast.from_pretrained(self.model_id, **kwargs)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def test_save_load_pretrained_default(self):
+        # NOTE: feature_extractor and image_processor both use the same filename, preprocessor_config.json, when saved to
+        # disk, but the files are overwritten by processor.save_pretrained(). This test does not attempt to address
+        # this potential issue, and as such, does not guarantee content accuracy.
+
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+        image_processor = self.get_image_processor()
+
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        processor.save_pretrained(self.tmpdirname, legacy_serialization=False)
+        processor = Gemma3nProcessor.from_pretrained(self.tmpdirname)
+
+        self.assertIsInstance(processor.tokenizer, GemmaTokenizerFast)
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+
+        self.assertIsInstance(processor.feature_extractor, Gemma3nAudioFeatureExtractor)
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+
+    def test_save_load_pretrained_additional_features(self):
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+        image_processor = self.get_image_processor()
+
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+        processor.save_pretrained(self.tmpdirname, legacy_serialization=False)
+
+        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS-BOS)", eos_token="(EOS-EOS)")
+        feature_extractor_add_kwargs = self.get_feature_extractor(dither=5.0, padding_value=1.0)
+
+        processor = Gemma3nProcessor.from_pretrained(
+            self.tmpdirname, bos_token="(BOS-BOS)", eos_token="(EOS-EOS)", dither=5.0, padding_value=1.0
+        )
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
+        self.assertIsInstance(processor.tokenizer, GemmaTokenizerFast)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, Gemma3nAudioFeatureExtractor)
+
+    @parameterized.expand([256, 512, 768, 1024])
+    def test_image_processor(self, image_size: int):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        raw_image = np.random.randint(0, 256, size=(image_size, image_size, 3), dtype=np.uint8)
+        input_image_processor = image_processor(raw_image, return_tensors="pt")
+        input_processor = processor(text="Describe:", images=raw_image, return_tensors="pt")
+
+        for key in input_image_processor:
+            self.assertAlmostEqual(input_image_processor[key].sum(), input_processor[key].sum(), delta=1e-2)
+            if "pixel_values" in key:
+                # NOTE: all images should be re-scaled to 768x768
+                self.assertEqual(input_image_processor[key].shape, (1, 3, 768, 768))
+                self.assertEqual(input_processor[key].shape, (1, 3, 768, 768))
+
+    def test_audio_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        raw_speech = floats_list((3, 1000))
+        input_feat_extract = feature_extractor(raw_speech, return_tensors="pt")
+        input_processor = processor(text="Transcribe:", audio=raw_speech, return_tensors="pt")
+
+        for key in input_feat_extract:
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+    def test_tokenizer(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        input_str = "This is a test string"
+
+        encoded_processor = processor(text=input_str)
+
+        encoded_tok = tokenizer(input_str)
+
+        for key in encoded_tok:
+            self.assertListEqual(encoded_tok[key], encoded_processor[key][0])
+
+    def test_tokenizer_decode(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
+
+        decoded_processor = processor.batch_decode(predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)